similar to: Bug in package stats function ar() (PR#10459)

Dears Sirs During my computational work I encountered unexpected behavior when calling "ar" function, namely # time series x<-ts(c(-0.2052083,-0.3764986,-0.3762448,0.3740089,0.2737568,2.8235722,- 1.7783313,0.2728676,-0.3273164),start=c(1978,3),frequency=4,end=c(1980,3)) # ar function res.ar<-ar(x,aic=TRUE,demean=F) # call "ar" again and ............

Dears Sirs During my computational work I encountered unexpected behaviour when calling "ar" function. I want to select the order p of the autoregressive approximation by AIC criterion and sometimes an error occurs. Example: # time series

Hello, I understand that the/ demean/ argument in the *ar()* function to fit an autoregressive model selects the best AR model fitted to the mean deleted observations. What is the purpose of using this demean procedure at all? Its seems silly as the post doesn't deal with R problems.... Thanks -- View this message in context:

Hello, I propose two small changes to spec.pgram to get modest speedup when dealing with input (x) having multiple columns. With plot = FALSE, I commonly see ~10-20% speedup, for a two column input matrix and the speedup increases for more columns with a maximum close to 45%. In the function as it currently exists, only the upper right triangle of pgram is necessary and pgram is not returned by

I'm tring the functions to check the cointegration of a matrix. I'm using **Phillips & Ouliaris Cointegration Test** The function in *tseries* package is **po.test** and **ca.po** in *urca* The results with **URCA** are: > ca.po(prices, demean='none') ######################################## # Phillips and Ouliaris Unit Root Test #

Hi there, we are beginners in R and we are trying to fit the following time series using ar(2): > x <- c(1.89, 2.46, 3.23, 3.95, 4.56, 5.07, 5.62, 6.16, 6.26, 6.56, 6.98, > 7.36, 7.53, 7.84, 8.09) The reason of choosing the present time series is that the we have previously calculated analitically the autoregressive coefficients using the direct inversion method as 1.1, 0.765, 0.1173.

Dear R users I have two questions about estimating the spectral power of a time series: 1) I came across a funny thing with the following code: data(co2) par(mfrow=c(2,1)) co2.sp1<-spectrum(co2,detrend=T,demean=T,span=3) co2.sp2<-spectrum(co2[1:468],detrend=T,demean=T,span=3) The first plot displays the frequencies ranging from 0 to 6 whearas the second plot displays the same curve but

Dear R-helpers, First of all, sorry for those who have (eventually) already received that request. The mail has been bumped several times, so I am not sure the list has received it... and I need help (if you have time)! ;-) I have a very simple question and I really hope that someone could help me I would like to estimate a simple fixed effect regression model with clustered standard errors by

Full_Name: Ian McLeod Version: 2.3.1 OS: Windows Submission from: (NULL) (129.100.76.136) > There is a simple bug in acf as shown below: > > z <- 1 > acf(z,lag.max=1,plot=FALSE) > Error in acf(z, lag.max = 1, plot = FALSE) : > 'lag.max' must be at least 1 > This is certainly a bug. There are two problems: (i) the error message is wrong since lag.max is

Dear R-helpers, I have a very simple question and I really hope that someone could help me I would like to estimate a simple fixed effect regression model with clustered standard errors by individuals. For those using Stata, the counterpart would be xtreg with the "fe" option, or areg with the "absorb" option and in both case the clustering is achieved with "vce(cluster

Hi, I'm trying to obtain confidence intervals for auto and cross correlation estimates. I've adapted code made available by Stock and Watson that uses the Bartlett Kernel and the delta method. In R it runs really, really slow because of the loops it uses and I have 9 series that I'd like to examine (81 total combinations). It was easy enough to replace one of the while loops with a

Dear R users, I wish to manually demean a panel over time and entities. I tried to code the Wansbeek and Kapteyn (1989) transformation (from Baltagi's book Ch. 9). As a benchmark I use both the pmodel.response() and model.matrix() functions in package plm and the results from using dummy variables. As far as I understood the transformation (Ch.3), Q%*%y (with y being the dependent variable)

In the R Document, the usage of the acf() is as follow: acf(x, lag.max = NULL, type = c("correlation", "covariance", "partial"), plot = TRUE, na.action = na.fail, demean = TRUE, ...) But now I want to get the result picture like: plot(x,type="l") or plot(x,type="p") How can I do this with acf function? 仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭仭佒伮 伬侎仯仭

Owen Anderson wrote: > There's nothing particularly stopping you from having your > installation package include copies of gas and ld, I disagree. gas and ld are not available on Windoze, except via MinGW. Yes I can make or tell my customers to install MinGW, but if MinGW is installed, then I don't need LLVM. (More about this further ahead) > You're welcome to think

I started to check what I thought I knew with autocovariance and it doesn’t jive with the the calculations given by ‘R’. I was wondering if there is some scaling or something that I am not aware of. Take the example Ø d <- 1:10 Ø (a <- acf(d, type="covariance", demean=FALSE, plot=FALSE)) Autocovariances of series ‘d’, by lag 0 1 2 3 4 5 6

Hello, My name is Pedro and it is nice to meet you all. I am having trouble understanding a message that I receive when use function ar.ols from package stats, it says that "Warning message: In ar.ols(x = dtb[2:6966, ], demean = FALSE, intercept = TRUE, prewhite = TRUE) : model order: 2 singularities in the computation of the projection matrix results are only valid up to model order 1,

?s 16:34 de 22/02/2024, Pedro Gavronski. escreveu: > Hello, > > My name is Pedro and it is nice to meet you all. I am having trouble > understanding a message that I receive when use function ar.ols from > package stats, it says that "Warning message: > In ar.ols(x = dtb[2:6966, ], demean = FALSE, intercept = TRUE, > prewhite = TRUE) : > model order: 2

Hello I am facing a curious problem.I have a time series data with which i want to fit auto-regressive model of order p, where p runs from 1:9.I am using a for loop which will fit an AR(p) model for each value of p using the *ar.ols* function. I am using the following code for ( p in 1:9){ a=ar.ols (x=data.ts, order.max=p, demean=T, intercept=T) } Specifying the *order.max* to be p, it gives me a

Hello, Thanks for the reply Rui and for pointing out that I forgot to attach my code. Please find attached in this email my code and data. Thanks in advance. Best regards, Pedro Gerhardt Gavronski. On Fri, Feb 23, 2024 at 5:50?AM Rui Barradas <ruipbarradas at sapo.pt> wrote: > > ?s 16:34 de 22/02/2024, Pedro Gavronski. escreveu: > > Hello, > > > > My name is Pedro

I am reading some documentation about Cross Spectrum Analysis as a technique to compare spectra. My understanding is that it estimates the correlation strength between quasi-periodic structures embedded in two signals. I believe it may be useful for my signals analysis. I was referred to the R functions that implement this type of analysis. I tried all the examples which generated a series of